Understanding human brain function in cognition, behavior and illness remains an enduring challenge. This is because the brain is highly complex, with billions of neurons, interacting dynamically to receive, process, retrieve, transmit, and store information. Neurons signal via millisecond electrical impulses, communicate with other neurons in both local (mm-range) and distant (cm-range) regions. These interactions evolve instantaneously in response to external stimuli, drugs, or feedback from other brain regions, during creation and expression of memory, emotion and perception, across states of arousal, and over time due to plasticity, learning, development and aging. Thus, many scientific challenges in understanding brain function come about from the need to parse these complex dynamical interactions across diverse spatial and temporal scales, such as across superficial cortical and deeper subcortical regions, and on time scales ranging from milliseconds to seconds and minutes. Monitoring these complex dynamic interactions at the relevant spatial and temporal scales, and as they occur in normal, clinical and disease states is, however, technologically challenging using known methods.
Electrophysiological techniques to assess neuronal activity serve as direct indicators of neuronal currents, neuronal spiking or postsynaptic potentials, and have uniquely high temporal resolution on the order of milliseconds. These techniques can be either invasive or non-invasive.
Invasive electrophysiological measurement techniques (e.g., electrocorticography) can be employed in patient populations with surgically implanted intracranial electrodes. While these techniques provide high spatial and temporal resolution of regional neuronal dynamics, they are highly invasive, and thus are typically applied in critical clinical settings (such as during surgery). This limits the types of patients/subjects, cognitive tasks, and behaviors that can be monitored with such techniques. Further, invasive electrophysiological techniques are limited to the focal regions wherein recording electrodes are placed (typically superficial cortical areas). Therefore, these techniques have limited spatial span for characterizing deep subcortical regions that have critical roles in a variety of cognitive and clinically relevant brain states.
Non-invasive electrophysiological measurement techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) are widely used in human neuroscience studies. M/EEG techniques are non-invasive, and can therefore be used to monitor and measure brain activities across a variety of patients/subjects, as well as in many types of cognitive tasks and behaviors. M/EEG techniques comprise data from sensors distributed across the head, and measure, with millisecond-resolution, electromagnetic fields generated by neuronal currents all over the brain. However, the data do not directly pinpoint spatial locations or regions where the neuronal currents originate. Instead, the locations have to be spatially resolved by computing appropriate solutions to the electromagnetic inverse problem of recovering regional current distributions from M/EEG data. This method is termed as electromagnetic source imaging, and offers a unique means to non-invasively probe regional brain dynamics with high temporal resolution.
FIG. 1 illustrates the general electromagnetic source imaging paradigm. Neuronal activity within the brain is modeled as a distribution of dipole currents, denoted by current dipoles (‘sources’) placed in structures across the brain. The dipole source locations are obtained using anatomic magnetic resonance imaging (MRI). Then, a numerical solution of Maxwell's equations (‘forward solution’) relates dipole source currents in each brain region to their associated M/EEG measurements. This solution is termed the forward solution, and gives rise to the forward matrix. Then, by incorporating a model of measurement noise, one arrives at a measurement equation. This measurement equation can then be inverted given the M/EEG data to obtain estimates of neuronal source currents across the brain. Thus, this technique is also often referred to as source estimation, source modeling or source localization.
The development of accurate forward solutions that account for brain anatomy and cortical surface geometry, along with significant advances in statistical signal processing, has led to reliable inverse solutions for cortical current distributions. Thus, electromagnetic source imaging is widely used for resolving neuronal dynamics in superficial cortical structures. However, because M/EEG signal amplitudes attenuate steeply with increasing source-sensor distance, it has been challenging to use electromagnetic source imaging for assessing neuronal dynamics in deeper subcortical regions that are farther away from non-invasive M/EEG sensors.
Overall, although existing electrophysiology-based techniques provide excellent temporal resolution, they have limited spatial resolution for activity in deep brain regions. Therefore, there remains a need for improved non-invasive methods and techniques to access and characterize deep brain activity with jointly high temporal and spatial resolution.